Enterprise AI Strategy: Page

4 strategic approaches to effective shadow AI governance

Shadow AI is a technological risk and a governance challenge. Organizations must combine structured frameworks, continuous visibility and education to control these risks.

By Nihad Hassan • April 15, 2026

GenAI data center infrastructure reshapes business processes

Building and retrofitting data centers to support GenAI is not a matter of IF but WHEN, so the game plan must include power needs, grid connectivity, model training and permitting.

By George Lawton • April 10, 2026

GenAI in product manufacturing cuts costs but adds risks

In product design and manufacturing, GenAI systems are consolidating supplier data, improving processes and cutting significant software costs -- yet they raise unique concerns.

By Patrick Thibodeau • April 7, 2026

Managing drift in AI models and data

The training data and algorithms used to build AI models have a shelf life. Detecting and correcting model drift ensures that these systems stay accurate, relevant and useful.

By Stephen Bigelow • April 2, 2026

Overcome roadblocks to GenAI adoption and unlock ROI

GenAI deployments can fall prey to unrealistic goals, misguided pilots, job loss fears, hidden costs and lack of trust. Governance and workforce readiness are keys to success.

By Kathleen Richards • April 2, 2026

FinOps can manage AI computing costs, experts say

FinOps is no longer just about optimizing cloud spending. Hear from experts on what makes FinOps a desirable option to help businesses manage their AI costs.

By Mary Pratt • April 1, 2026

7 best practices to avoid AI vendor lock-in

While lock-in is sometimes unavoidable, it's the very definition of risk. Businesses can use these best practices to mitigate lock-in risk with AI vendors.

By Stephen Bigelow • March 30, 2026

AI risk management: A strategic guide for enterprise leaders

Enterprises across business sectors are adopting AI technologies, but are they prepared to deal with the risks? Our AI risk management guide can help.

By Nihad Hassan • March 27, 2026

Businesses face complex cost-cutting options with GenAI

Some GenAI cost saving candidates might disappoint. The best approach to cost reductions vary according to the business function and an enterprise's strategic business priorities.

By John Moore • March 26, 2026

GenAI is ready for more autonomy, but supply chains are not

Disruptions, vulnerabilities, late deliveries and customer interactions are among the supply chain concerns that GenAI could address autonomously -- if businesses can trust it.

By Patrick Thibodeau • March 24, 2026

GenAI’s role in a return trek to the moon and beyond

NASA's use of GenAI models in the Artemis space program and communication with Mars rovers provides valuable business lessons in governance and keeping humans in the loop.

By Sean Kerner • March 24, 2026

U.S. federal AI framework deemed aspirational, noncommittal

The latest executive order is a step toward federal AI regulation. But it's largely noncommittal and shifts most responsibility to Congress, creating an interesting midterm dynamic.

By Jennifer English • March 24, 2026

5 questions every CIO should ask before investing in AI

AI investment is accelerating, but many initiatives fail to scale. Five key questions help CIOs evaluate use cases, data readiness, long-term value and risk before committing resources.

By Kinza Yasar • March 23, 2026

Build accountability into AI to drive business value

In a perfect world, AI systems work 100% of the time, but that's not the case in the real world. AI accountability ensures someone takes responsibility when AI fails.

By Kashyap Kompella • March 20, 2026

Does your organization need an AI ethics committee?

Ethics oversight ensures high-risk AI use is accountable, responsible and compliant. Not all organizations need one; some incorporate this oversight into risk or legal teams.

By Kinza Yasar • March 20, 2026

Smarter robots: Agentic and physical AI converge in business

The robotics industry is entering a new era where, thanks to AI, robots learn, optimize and solve the world's most complex supply chain, logistics and labor challenges in real time.

By Bridget Botelho • March 18, 2026

Is the AI memory shortage stifling enterprise adoption?

Enterprises bought up computer memory to fuel AI adoption. Now, amid a memory shortage, analysts share insights on what this means for enterprise AI adoption and what's next.

By Everett Bishop • March 16, 2026

AI-ready data needs its own set of rules, experts say

At Gartner's 2026 Data and Analytics Summit, AI-ready data was top of mind. Explore these best practices from experts on achieving data readiness for AI systems.

By Olivia Wisbey • March 13, 2026

C-suite shakeup: Demand for chief AI officers accelerates

GenAI's infiltration into virtually every aspect of business demands the C-suite make room for a CAIO focused on AI development, strategy, implementation, education and governance.

By Stephen Bigelow • March 13, 2026

Time for AI: The ‘too busy’ problem is a software-age hangover

Higher education must prioritize enterprise AI as a strategic shift, not just tool buying. Learn how governance, alignment and a five-year plan can transform institutions.

By Robert Clougherty • March 13, 2026

Why and how enterprises should build a green AI initiative

Executives making AI decisions must consider ethics, compliance and now environmental impact. Learn how green AI aligns governance and sustainability to improve digital strategies.

By Damon Garn • March 12, 2026

Q&A: Prioritize AI adoption while safeguarding human skills

In this Q&A, Gartner analyst Arun Chandrasekaran unpacks some of generative AI's invisible undercurrents and what to do about them.

By Olivia Wisbey • March 11, 2026

GenAI streamlines enterprise knowledge management process

Business leaders sharpen insights, speed decisions and boost productivity using GenAI-powered knowledge management systems with their ability to unify and synthesize data.

By Kinza Yasar • March 6, 2026

Tools and techniques for optimizing AI data pipelines

Enhancing the five critical stages of AI data pipelines is essential; it determines whether AI drives business value or becomes a drain on resources.

By Stephen Bigelow • March 4, 2026

Why multimodal AI is reshaping enterprise intelligence

Multimodal AI can transform industries by combining text, images, video and audio to solve complex problems single-modality systems often can't address.

By Stephen Catanzano • March 4, 2026